This disclosure relates generally to inertial measurement units, and more particularly to inertial measurement units that utilize air data parameters to produce error-compensated output values.
Many complex vehicle guidance systems, such as aircraft inertial navigation systems, utilize an inertial measurement unit (IMU) that senses and outputs current acceleration forces experienced by the IMU as well as changes in rotational position (e.g., roll, pitch, and yaw). Such IMUs typically sense the current acceleration in three dimensions via a triad of accelerometers, each oriented along one of three mutually-orthogonal axes. Similarly, changes in rotational position are typically sensed via a triad of gyroscopes, each oriented along one of the three mutually-orthogonal axes.
Outputs of the IMU (e.g., a three-axis acceleration vector as well as a three-axis vector representing changes in rotational speed) are often integrated over time by an inertial navigation system to arrive at a position and orientation of the vehicle relative to a known starting position and orientation via, e.g., dead reckoning techniques. However, such integration techniques can compound sensor errors over time. Some sensor errors, such as those due to temperature, can be considered deterministic in nature, and therefore compensated for in the integration techniques via pre-defined correction factors. Other errors, such as turn-on to turn-on biases and scale factor errors can be unpredictable or stochastic in nature, thereby preventing the use of such pre-defined correction factors for effective error compensation operations. The use of a fiber optic gyroscope (FOG) or ring laser gyroscope (RLG) can provide greater accuracy and consistency of measurements than, e.g., micro-electro-mechanical system sensors, but at significant added cost. Accordingly, accuracy of measurement is typically sacrificed for the benefit of reduced cost when utilizing MEMS sensors for measuring acceleration forces and rotational position changes in IMUs.